In May 2024, DBS became the first Singapore-listed company to achieve SGD 100 billion in market capitalisation. While market cap is just one of many factors to consider when evaluating a company’s performance, I was curious to see how the market cap of Singapore’s local banks has changed over the years.

I used R and the fmpcloudr package to retrieve market cap data, then created a Plotly chart to make it easier to explore the numbers at specific points in time.

Chart

The below chart is best viewed on desktop.

Hover mouse on chart to see data for specific dates

   

Load in required libraries and set token for fmpcloudr

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library(tidyverse)
library(lubridate)
library(fmpcloudr)
library(ggtext)
library(scales)
library(plotly)

fmpc_set_token('<insert token here>') # get token from: https://site.financialmodelingprep.com/developer/docs

Prepare data set for plotting

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symbols = c('D05.SI', 'U11.SI', 'O39.SI')
data = fmpc_security_mrktcap(symbols, limit = 30*12*12)
data = data %>% mutate(date = as.Date(date, format ='%Y-%m-%d')) %>%
  mutate(name = case_when(symbol == 'D05.SI' ~ "DBS",
                          symbol == 'U11.SI' ~ "UOB",
                          symbol == 'O39.SI' ~ "OCBC"))

Plot chart

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fig = ggplot(data, aes(date, marketCap, colour = name, group = 1, 
                       text = paste0("Date: ", format(date, "%d %b %Y"), "<br>Stock: ", name, "<br>Market Cap: <b>", paste(format(round(marketCap/1e9,1),trim = TRUE), "B"),"</b>"))) +
  geom_line() + 
  labs(title="<b>Market Cap Trend - <span style='color: #000000'>DBS</span>, <span style='color: #e11a27;'>OCBC</span> and <span style='color: #0060AE;'>UOB</span>",
       x=" ", y = "Market Cap")+ 
  scale_color_manual(values = c("#000000", "#e11a27", "#0060AE")) + 
  theme(plot.subtitle=element_text(size=18, hjust=0.5, face="italic", color="black")) +
  theme_bw() +
  theme(
    plot.title = element_markdown(size = 12),
    legend.position = "none",
  )+
  scale_y_continuous(labels = scales::label_number(scale_cut = cut_short_scale())) +
  scale_x_date(date_breaks = "6 month", 
               date_labels = "%b%y" , expand = c(0.1,0))+
  geom_hline(yintercept=100*1e9,linetype='dashed' ,col ='grey') +
  annotate("text", x = as.Date("2023-01-01"), y = 102*1e9 , label = '100 Billion', col = 'grey', size = 3)

ggplotly(fig, width = 650, height = 500, tooltip = "text") %>% layout(hovermode = "x unified")